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The prediction of the density of undersaturated crude oil using multilayer feed-forward back-propagation perceptron

Rostami, H ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1080/10916461003773021
  3. Publisher: 2011
  4. Abstract:
  5. Crude oil density is an important thermodynamic property in simulation processes and design of equipment. Using laboratory methods to measure crude oil density is costly and time consuming; thus, predicting the density of crude oil using modeling is cost-effective. In this article, we develop a neural network-based model to predict the density of undersaturated crude oil. We compare our results with previous works and show that our method outperforms them
  6. Keywords:
  7. Density ; Neural network ; Prediction ; Undersaturated crude oil ; Feedforward backpropagation ; Laboratory methods ; Network-based ; Oil density ; Perceptron ; Simulation process ; Density (specific gravity) ; Forecasting ; Neural networks ; Crude oil
  8. Source: Petroleum Science and Technology ; Volume 30, Issue 1 , 2011 , Pages 89-99 ; 10916466 (ISSN)
  9. URL: http://www.tandfonline.com/doi/abs/10.1080/10916461003773021